Course : (ΜΕΕ101) Methodology of Educational Research: Quantitative Approaches (MER:QA)
Curriculum : The aim of the course is to familiarize postgraduate students with the methods of analysis of research data collected during the investigation of social phenomena following the quantitative paradigm.
Upon successful completion of the course, the postgraduate student will be able to:
• Describes the basic steps for conducting quantitative research.
• Selects an appropriate sampling method for a representative sample.
• Identifies and evaluates the experimental research design.
• Formulates appropriate research hypotheses according to the research problem.
• Recognizes and utilizes the basic hypothesis testing procedure.
• Performs basic inferential procedures in a digital data analysis environment.
• Uses multivariate data analysis methods.
• Presents the results of the analysis linking them to the research problem.
• Critically approaches contemporary issues in educational research.
• Designs and implements educational research by selecting appropriate research tools.
Outline : Basic concepts of scientific research: The quantitative research method, population and sample, sampling methods, variables and scales, research tools, reliability and validity of measurement tools. Descriptive Statistics: Frequency tables, measures of central location, dispersion, and shape, and graphical presentation of data.
Inferential Statistics: Central limit theorem, research hypotheses, and statistical significance. Normal distribution and procedures for testing it utilizing and tests such as: Kolmogorov-Smimov and Shapiro-Wilk.
Testing of independent and dependent samples and testing of relationship between qualitative and between quantitative variables. Parametric tests such as: t-test and ANOVA as well as non-parametric tests such as: Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman. Finally, Pearson and Spearman correlation coefficients as well as chi-square test. Multivariate analysis: multivariate linear regression, factor and cluster analysis.
Utilizing of the SPSS for analyzing data.
Teaching and learning methods:
• Lectures and laboratory.
• Group work.
• Exercises and applications with real data regarding various educational issues.
Methods of Assessments:
• Postgraduate students are obliged to attend all the teaching meetings.
• Each postgraduate student will have to carry out three written works:
First two written works. In the first work in a hypothetical survey, each postgraduate student will describe, using descriptive statistics, the distribution of some variables and any relationship they observe between them. In the second work, each postgraduate student will test research hypotheses by utilizing inferential statistics. The teacher will provide the data for the two individual assignments which will be different for each postgraduate student. The first two assignments constitute 40% of the final assessment.
Third written work. The third and final assignment may be collaborative, with up to two postgraduate students. In this assignment, the postgraduate students, following the quantitative paradigm, will plan and implement a small-scale research study investigating the topic of their interest. They will collect data, using structured research tools, which they will analyze and present the results of their research as a simulation of a published paper. This work (10-12 pages in length) will be presented to all postgraduate students in two workshops organized for this purpose. The evaluation of the third assignment will be based both on the deliverables and on the oral examination of the postgraduate students during the presentation of their work to all students. This assignment will constitute 60% of the final assessment.
Suggested bibliography
American Psychological Association (2020). Publication manual of the American Psychological Association 2020: the official guide to ΑΡΑ style (7th éd.). American Psychological Association, https://apastyle.apa.org/products/publication-manual-7th-edition
Bryman, A. (2016). Social research methods. UK: Oxford University Press.
Cohen, L. & Manion, L. (1994). Research methods in education, in Greek, Athens: Μεταίχμιο.
Creswell, J. W. (2002). Educational Research: Planning, Conducting, and Evaluating Quantitative. Upper Saddle River, NJ:Prentice-Hall.
Dafermos, V. (2005). Social Statistics with SPSS, in Greek, Thessaloniki: ΖΗΤΗ.
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
Γιαλαμάς, Β., Λαβίδας, Κ., & Μάνεσης, Δ. (2023). ΣΤΑΤΙΣΤΙΚΕΣ ΜΕΘΟΔΟΙ ΚΑΙ ΤΕΧΝΙΚΕΣ ΣΤΙΣ ΚΟΙΝΩΝΙΚΕΣ ΕΠΙΣΤΗΜΕΣ ΜΕ ΤΗ ΧΡΗΣΗ SPSS. ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΗΛΕΚΤΡΟΝΙΚΑ ΣΥΓΓΡΑΜΜΑΤΑ - ΚΑΛΛΙΠΟΣ+» (υπό έκδοση).
Gnardelis C. (2003). Applied statistics, in Greek, Athens: Παπαζήση.
Kanji, G. K. (2006). 100 statistical tests. Sage.
Schumacker, R., & Lomax, R. (2018). A beginner's guide to structural equation modeling (4th éd.). New York: Routledge.
Related academic journals and published research works
Notes of lecturers in Greek.
Lavidas, K., Papadakis, S., Manesis, D., Grigoriadou, A. & Gialamas, V. (2022). The effect of social desirability on students’ self-reports in two social contexts: Lectures vs. lectures and lab classes, Information 1(10), 491. https://doi.org/10.3390/info13100491